{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The Black-Scholes PDE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[*Black Scholes PDE, WIKI*](https://en.wikipedia.org/wiki/Black%E2%80%93Scholes_equation). \n",
    "\n",
    "In this notebook I want to show how to solve the famous Black-Scholes (BS) Partial Differential Equation (PDE).\n",
    "\n",
    "\\begin{equation}\n",
    "\\frac{\\partial  V(t,s)}{\\partial t}  \n",
    "          + r\\,s \\frac{\\partial V(t,s)}{\\partial s}\n",
    "          + \\frac{1}{2} \\sigma^2 s^2 \\frac{\\partial^2  V(t,s)}{\\partial s^2} - r  V(t,s)  = 0.\n",
    "\\end{equation}\n",
    "\n",
    "I decided to use a fully implicit method, because it is an efficient and stable method. The illustrated approach can be used to solve more complex problems, such as the pricing of different types of derivatives (barrier options, digital options, American options, etc) with small modifications.\n",
    "\n",
    "## Contents\n",
    "   - [The BS PDE](#sec1)\n",
    "      - [PDE in log-variables](#sec1.1)\n",
    "      - [Implicit discretization](#sec1.2)\n",
    "   - [Introduction to sparse matrices](#sec2)\n",
    "   - [Numerical solution of the PDE](#sec3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "from scipy import sparse\n",
    "from scipy.sparse.linalg import splu\n",
    "from scipy.sparse.linalg import spsolve\n",
    "\n",
    "from IPython.display import display\n",
    "import sympy\n",
    "\n",
    "sympy.init_printing()\n",
    "\n",
    "\n",
    "def display_matrix(m):\n",
    "    display(sympy.Matrix(m))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='sec1'></a>\n",
    "## The BS PDE\n",
    "\n",
    "Ok ok... *Nihil novi sub sole*. The PDE derivation can be found in every textbooks... I know.  \n",
    "\n",
    "I suggest the interested reader to have a look at [1] for a clear exposition. If you are already familiar with these concepts, maybe it can be interesting for you to have a look at the notebook **A3**, where I present the derivation of the P(I)DE for general exponential Lévy processes. The BS PDE appears as a special case."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='sec1.1'></a>\n",
    "## PDE in log-variables\n",
    "\n",
    "Let us consider the geometric Brownian motion (GBM), described by the SDE:    \n",
    "\n",
    "\\begin{equation}\n",
    "\t  \\frac{dS_t}{S_t} = \\mu dt + \\sigma dW_t.\n",
    "\\end{equation}\n",
    "\n",
    "Let $V(t,s)$ be the value of a European call option. \n",
    "\n",
    "By the **martingale pricing theory**, in an arbitrage-free market there exists an equivalent martingale measure $\\mathbb{Q}$ such that the discounted option price is a $\\mathbb{Q}$-martingale. (Remember that under $\\mathbb{Q}$ the drift $\\mu$ is replaced by the risk free drift $r$.)\n",
    "\n",
    "The PDE for the option price is:\n",
    "\n",
    "\\begin{equation}\n",
    " \\frac{\\partial V(t,s)}{\\partial t} + \\mathcal{L}^{S} V(t,s) -r V(t,s) = 0   \n",
    "\\end{equation}\n",
    "\n",
    "where $\\mathcal{L}^{S}$ is the infinitesimal generator of $\\{S_t\\}_{t\\in[t_0,T]}$.  \n",
    "In the Black-Scholes model, the measure $\\mathbb{Q}$ is unique and the infinitesimal generator under this measure is:\n",
    "\n",
    "\\begin{equation}\n",
    " \\mathcal{L}^{BS} V(t,s) = \\;  r s \\frac{\\partial V(t,s)}{\\partial s}\n",
    "+ \\frac{1}{2} \\sigma^2 s^2 \\frac{\\partial^2  V(t,s)}{\\partial s^2},\n",
    "\\end{equation}\n",
    "\n",
    "In order to simplify the computations, it is better to pass to the log-variable $x = \\log s$. This change corresponds to the following change in the operators:\n",
    "\n",
    "\\begin{equation}\n",
    "s \\frac{\\partial}{\\partial s} = \\frac{\\partial}{\\partial x}, \\hspace{2em} \n",
    "s^2 \\frac{\\partial^2}{\\partial s^2} = \\frac{\\partial^2}{\\partial x^2} - \\frac{\\partial}{\\partial x} . \n",
    "\\end{equation}\n",
    "\n",
    "In log-variables the BS PDE is:\n",
    "\n",
    "$$ \\frac{\\partial  V(t,x)}{\\partial t}  \n",
    "          + \\biggl( r -\\frac{1}{2}\\sigma^2 \\biggr) \\frac{\\partial V(t,x)}{\\partial x}\n",
    "          + \\frac{1}{2} \\sigma^2 \\frac{\\partial^2  V(t,x)}{\\partial x^2} - r  V(t,x)  = 0. $$\n",
    "\n",
    "For an option with strike $K$ and maturity $T$, the boundary conditions are:\n",
    "\n",
    "CALL:\n",
    " - Terminal:\n",
    " $$ V(T,x) = \\max(e^x-K,0), $$\n",
    " - Lateral:\n",
    " $$ V(t, x) \\underset{x \\to -\\infty}{=} 0 \\quad \\text{and} \\quad V(t, x) \\underset{x \\to \\infty}{\\sim} e^x - Ke^{-r(T-t)}. $$\n",
    "\n",
    "PUT:\n",
    " - Terminal:\n",
    " $$ V(T,x) = \\max(K-e^x,0), $$\n",
    " - Lateral:\n",
    "  $$ V(t, x) \\underset{x \\to -\\infty}{\\sim} Ke^{-r(T-t)} \\quad \\text{and} \\quad V(t, x) \\underset{x \\to \\infty}{=} 0. $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Derivative approximation\n",
    "\n",
    "Finite difference methods are a technique for obtaining numerical solutions of PDEs. \n",
    "The idea underlying finite-difference methods is to replace the partial derivatives occurring in the PDE by finite difference approximations. If we assume that $V$ is a smooth function, we can use the Taylor series expansion near the point of interest.\n",
    "For a $\\Delta t > 0$ we can write\n",
    "\n",
    "\\begin{equation}\n",
    " V(t+\\Delta t,x) \\approx V(t,x) + \\frac{\\partial V(t,x)}{\\partial t} \\Delta t + \\frac{1}{2} \\frac{\\partial^2 V(t,x)}{\\partial t^2} \\Delta t^2 + \\mathcal{O}(\\Delta t^3).\n",
    "\\end{equation}\n",
    "\n",
    "\\begin{equation}\n",
    " V(t-\\Delta t,x) \\approx V(t,x) - \\frac{\\partial V(t,x)}{\\partial t} \\Delta t + \\frac{1}{2} \\frac{\\partial^2 V(t,x)}{\\partial t^2} \\Delta t^2 + \\mathcal{O}(\\Delta t^3).\n",
    "\\end{equation}\n",
    "\n",
    "An analogous approximation can be done for $V(t,x+\\Delta x)$ with $\\Delta x > 0$.\n",
    "\n",
    "If we want to approximate the partial derivative with respect to time, we obtain the following finite difference approximation\n",
    "\n",
    "\\begin{equation}\n",
    " \\frac{\\partial V(t,x)}{\\partial t} \\approx \\frac{V(t+\\Delta t,x) - V(t,x)}{\\Delta t} + \\mathcal{O}(\\Delta t)\n",
    "\\end{equation}\n",
    "\n",
    "also called **forward difference**, since the differencing is in the forward $t$ direction.\n",
    "We can also consider the **backward difference**\n",
    "\n",
    "\\begin{equation}\n",
    " \\frac{\\partial V(t,x)}{\\partial t} \\approx \\frac{V(t,x) - V(t-\\Delta t,x)}{\\Delta t} + \\mathcal{O}(\\Delta t)\n",
    "\\end{equation}\n",
    "\n",
    "and the **central difference**\n",
    "\n",
    "\\begin{equation}\n",
    " \\frac{\\partial V(t,x)}{\\partial t} \\approx \\frac{V(t+\\Delta t,x) - V(t-\\Delta t,x)}{2 \\Delta t} + \\mathcal{O}(\\Delta t^2).\n",
    "\\end{equation}\n",
    "\n",
    "The use of the forward and backward difference approximation leads to the **explicit** and **implicit** finite difference \n",
    "schemes respectively. The central difference is not used for the time variable because it leads to bad numerical schemes.\n",
    "But it is common to use it for the space variable.\n",
    "\n",
    "For second order derivatives, such as $\\partial^2 V(t,x)/\\partial x^2$, we can use the symmetric central difference approximation for a $\\Delta x > 0$:\n",
    "\n",
    "\\begin{equation}\n",
    " \\frac{\\partial^2 V(t,x)}{\\partial x^2} \\approx \\frac{V(t,x+\\Delta x) + V(t,x-\\Delta x) - 2V(t,x)}{ \\Delta x^2} + \\mathcal{O}(\\Delta x^2).\n",
    "\\end{equation}\n",
    "\n",
    "If you need more details, have a look at [2]."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='sec1.2'></a>\n",
    "## Implicit discretization\n",
    "\n",
    "\n",
    "First we have to restrict the theoretical infinite domain to the finite region $[t_0,T]\\, \\times \\, [A_1,A_2]$, with $A_1 < A_2$. \n",
    "\n",
    "The next step is to replace $[t_0,T]\\times [A_1,A_2]$ by a discrete grid:\n",
    "\n",
    "For $n = 0,1, ... N \\in \\mathbb{N}$, define the discrete time step $ \\Delta t = \\frac{T - t_0}{N} $ such that\n",
    "$t_n = t_0 + n \\Delta t$. For $i = 0,1, ... M \\in \\mathbb{N}$, define the discrete space step $ \\Delta x = \\frac{A_2 - A_1}{M} $ such that\n",
    "$x_i = A_1 + i \\Delta x$.\n",
    "\n",
    "The grid is divided into equally spaced nodes of distance $\\Delta x$ in the x-axis, and of distance $\\Delta t$ in the t-axis.\n",
    "\n",
    "The mesh points have the form $(t_0 + n \\Delta t, A_1 + i \\Delta x)$.\n",
    "At this point we concern ourselves only with the values of $V(t,x)$ on the mesh nodes. We call \n",
    "\n",
    "$$ V(t_0 + n \\Delta t, A_1 + i \\Delta x) = V^n_i .$$\n",
    "\n",
    "We apply the backward discretization (implicit scheme) for the time derivative, and a central discretization for the first order space derivative.  \n",
    "\n",
    "We are interested in the value of V at time $t_0$. We know the values $V^N$ corresponding to the terminal conditions. The algorithm consists in finding the values $V^n$ given the knowledge of the values $V^{n+1}$. \n",
    "\n",
    "**Comment:**\n",
    " A common practice is to invert the time flow i.e. introducing a new time variable $\\tau = -t$ and change the variables in the PDE. It follows that the terminal conditions become initial conditions, and the problem becomes a forward problem. However, I am not using this change of variable in this notebook.\n",
    "\n",
    "The discretized equation becomes\n",
    "\n",
    "$$ \\begin{aligned}\n",
    "\\frac{V^{n+1}_{i} -V^{n}_{i}}{\\Delta t} + \n",
    "(r-\\frac{1}{2}\\sigma^2) \\frac{V^{n}_{i+1} -V^{n}_{i-1}}{ 2 \\Delta x}\n",
    "+ \\frac{1}{2} \\sigma^2 \\frac{V^{n}_{i+1} + V^{n}_{i-1} - 2 V^{n}_{i}}{\\Delta x^2}  - r V^{n}_i = 0.\n",
    "\\end{aligned}$$\n",
    "\n",
    "Rearranging the terms: \n",
    "\n",
    "$$ \\begin{aligned}\n",
    " V^{n+1}_{i} &= V^{n}_{i} \\biggl( 1 + r\\Delta t + \\sigma^2 \\frac{\\Delta t}{\\Delta x^2} \\biggr)  \\\\\n",
    "& + V^{n}_{i+1} \\biggl( -(r -\\frac{1}{2}\\sigma^2)\\frac{\\Delta t}{2 \\Delta x} -\n",
    "\\frac{1}{2}\\sigma^2 \\frac{\\Delta t}{\\Delta x^2}  \\biggr)  \\\\\n",
    "& + V^{n}_{i-1} \\biggl( (r -\\frac{1}{2}\\sigma^2)\\frac{\\Delta t}{2 \\Delta x} - \n",
    "\\frac{1}{2}\\sigma^2 \\frac{\\Delta t}{\\Delta x^2}  \\biggr).\n",
    "\\end{aligned} $$\n",
    "\n",
    "We can rename the coefficients such that:   \n",
    "\n",
    "$$ V^{n+1}_{i} = a V^{n}_{i-1} + b V^{n}_{i} + c V^{n}_{i+1}, $$\n",
    "\n",
    "and write it in matrix form:\n",
    "\n",
    "$$\n",
    "\\left(\n",
    "\\begin{array}{c}\n",
    "V^{n+1}_{1} \\\\\n",
    "V^{n+1}_{2} \\\\\n",
    "\\vdots \\\\\n",
    "V^{n+1}_{M-2} \\\\\n",
    "V^{n+1}_{M-1} \\\\\n",
    "\\end{array}\n",
    "\\right) = \n",
    "\\underbrace{\n",
    "\\left(\n",
    "\\begin{array}{ccccc}\n",
    "b     & c  & 0 & \\cdots  & 0 \\\\\n",
    "a     & b  & c & 0  & 0  \\\\\n",
    "0      & \\ddots & \\ddots &   \\ddots     & 0  \\\\\n",
    "\\vdots & 0 & a & b  & c  \\\\\n",
    "0      & 0 & 0 & a  & b \\\\\n",
    "\\end{array}\n",
    "\\right) }_{\\mathcal{D}} \\cdot\n",
    "\\left(\n",
    "\\begin{array}{c}\n",
    "V^{n}_{1} \\\\\n",
    "V^{n}_{2} \\\\\n",
    "\\vdots \\\\\n",
    "V^{n}_{M-2} \\\\\n",
    "V^{n}_{M-1} \n",
    "\\end{array}\n",
    "\\right)\n",
    "+ \\underbrace{\n",
    "\\left(\n",
    "\\begin{array}{c}\n",
    " a V^{n}_{0} \\\\\n",
    "  0 \\\\\n",
    " \\vdots \\\\\n",
    " 0 \\\\\n",
    "c V^{n}_{M} \\\\\n",
    "\\end{array}\n",
    "\\right) }_{\\text{B (boundary terms)}}\n",
    "$$\n",
    "\n",
    "The system \n",
    "\n",
    "$$ V^{n+1} = \\mathcal{D} V^{n} + B $$\n",
    "\n",
    "can be solved easily for $V^{n}$ by inverting the matrix $\\mathcal{D}$.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='sec2'></a>\n",
    "## Introduction to sparse matrices \n",
    "\n",
    "A [*sparse matrix*](https://en.wikipedia.org/wiki/Sparse_matrix) is a matrix in which most of the entries are zeros.\n",
    "\n",
    "If most of the elements are nonzero, then the matrix is considered *dense*.\n",
    "\n",
    "A dense matrix is typically stored as a two-dimensional array. Each entry in the array represents an element of the matrix and is accessed by the two indices i and j.\n",
    "\n",
    "Since a sparse matrix is almost empthy, storing all the zeros is a waste of memory. For this reason it is more efficient to use different data structures from the usual 2D arrays. More information on these can be found in the wiki page. \n",
    "\n",
    "Have a look at the manual page of the **scipy.sparse** library. [link](https://docs.scipy.org/doc/scipy/reference/sparse.html).\n",
    "\n",
    "There are many ways to represent a sparse matrix, Scipy provides seven of them:\n",
    "- Block Sparse Row (BSR)\n",
    "- Coordinate list (COO)\n",
    "- Compressed Sparse Column (CSC)\n",
    "- Compressed Sparse Row (CSR)\n",
    "- DIAgonal storage (DIA)\n",
    "- Dictionary Of Keys (DOK)\n",
    "- List of lists (LIL)\n",
    "\n",
    "Each format has its pros and cons. But we can roughly divide them in two groups: \n",
    "- CSR and CSC are better for efficient matrix operations.\n",
    "- All the others are better for the initial construction the sparse matrix.\n",
    "\n",
    "The good thing is that it is very easy to convert one representation into one other. Here I pasted some recomandations from the scipy manual page:\n",
    "\n",
    "*To construct a matrix efficiently, use either dok_matrix or lil_matrix. The lil_matrix class supports basic slicing and fancy indexing with a similar syntax to NumPy arrays. The COO format may also be used to efficiently construct matrices. Despite their similarity to NumPy arrays, it is strongly discouraged to use NumPy functions directly on these matrices because NumPy may not properly convert them for computations, leading to unexpected (and incorrect) results. If you do want to apply a NumPy function to these matrices, first check if SciPy has its own implementation for the given sparse matrix class, or convert the sparse matrix to a NumPy array (e.g. using the toarray() method of the class) first before applying the method.\n",
    "To perform manipulations such as multiplication or inversion, first convert the matrix to either CSC or CSR format. The lil_matrix format is row-based, so conversion to CSR is efficient, whereas conversion to CSC is less so.\n",
    "All conversions among the CSR, CSC, and COO formats are efficient, linear-time operations.*\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Example: Matrix multiplication and operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0\\\\1 & 3 & 0 & 0 & 0\\\\1 & 1 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "⎡0  0  0  0  0⎤\n",
       "⎢             ⎥\n",
       "⎢0  0  0  0  0⎥\n",
       "⎢             ⎥\n",
       "⎢1  3  0  0  0⎥\n",
       "⎢             ⎥\n",
       "⎢1  1  0  0  0⎥\n",
       "⎢             ⎥\n",
       "⎣0  0  0  0  0⎦"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CSR representation:  (ordered by rows)\n",
      "  (2, 0)\t1\n",
      "  (2, 1)\t3\n",
      "  (3, 0)\t1\n",
      "  (3, 1)\t1\n",
      "CSC representation:  (ordered by columns)\n",
      "  (2, 0)\t1\n",
      "  (3, 0)\t1\n",
      "  (2, 1)\t3\n",
      "  (3, 1)\t1\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(seed=44)\n",
    "dense_mat = np.random.binomial(n=1, p=0.1, size=(5, 5))  # Sparse matrix stored as a dense matrix\n",
    "dense_mat[2, 1] = 3\n",
    "sparse_mat = sparse.csr_matrix(dense_mat)  # Sparse matrix stored as a CSR sparse matrix\n",
    "\n",
    "display_matrix(sparse_mat.toarray())\n",
    "print(\"CSR representation:  (ordered by rows)\")\n",
    "sympy.pprint(sparse_mat)\n",
    "print(\"CSC representation:  (ordered by columns)\")\n",
    "sympy.pprint(sparse_mat.tocsc())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feel free to convert the sparse matrix into a different representations and pprint it.\n",
    "\n",
    "Now let's see some math operations. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}0 & 0\\\\0 & 0\\\\8 & 8\\\\4 & 4\\\\0 & 0\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "⎡0  0⎤\n",
       "⎢    ⎥\n",
       "⎢0  0⎥\n",
       "⎢    ⎥\n",
       "⎢8  8⎥\n",
       "⎢    ⎥\n",
       "⎢4  4⎥\n",
       "⎢    ⎥\n",
       "⎣0  0⎦"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}7 & 13 & 0 & 0 & 0\\\\17 & 33 & 0 & 0 & 0\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "⎡7   13  0  0  0⎤\n",
       "⎢               ⎥\n",
       "⎣17  33  0  0  0⎦"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "two_dense = 2 * np.ones(shape=(5, 2), dtype=np.int32)  # Example of dense matrix\n",
    "range_dense = np.reshape(np.array([i for i in range(1, 11)]), (2, 5))  # Second example of dense matrix\n",
    "\n",
    "# The product is a dense matrix\n",
    "display_matrix(sparse_mat @ two_dense)\n",
    "display_matrix(range_dense @ sparse_mat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Depending on what you prefer to use, you can call *toarray* or *todense* to convert the sparse matrix in a np.array or in a np.matrix. \n",
    "\n",
    "Be careful because the the **'*'** operator represents the element-wise product for numpy arrays and the scalar product for the numpy matrices.\n",
    "\n",
    "I prefer to use the numpy arrays, and therefore I will use the **@** operator for the scalar product operation. This operator handles very well hybrid multiplications between sparse and dense matrices. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.matrix'>\n"
     ]
    }
   ],
   "source": [
    "print(type(sparse_mat.toarray()))\n",
    "print(type(sparse_mat.todense()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Example: Diagonal matrices\n",
    "A diagonal matrix can be initialized as follows, by giving as the second argument a list of diagonals to be filled, and as first argument the list of values in the respective diagonal. (enter ```sparse.diags??``` in a cell for more information)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Ix1Lf0y57D/+wEu40Rnytm7b92EyRaOnTyuRFbJ/cQcHIfFVJiEvNOIuzzvKnUVHpWT1jp0lax8/8A8bNJYoXrtWVaOmlbJ+riVe7QOElymDoGj1jJye949/gYe1VtPRs2+dyUGx3S7jz0IXxn/me/zSH0e72erY9nRM7xXsVLT3b9lkcFIwk4c7dv+jgfcAHm7bfYvluiHD807nttzAtH1IOGWldtNQ6tO3t3L7UsjxjSSJMmrwPao9Cwp29ipayCLC22CP+5kVL4XjoYBlud6u8wqQS7hxZ10oMiIE6GIBTlHBnHaZQLsSAGIgxkKUPKpYBpYkBMSAGQgzIQYWYUbwYEAPFGZCDKm4CZUAMiIEQA3JQIWYULwbEQHEGpp8ZcGKteYY4qJGTTimIATEgBi7OAPzLR1x0G7owHRTHwb0KHOByjFwAq6LFgBjIz4AJqSze+f+FtLFmljGmEgAAAABJRU5ErkJggg==",
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}1.0 & 5.0 & 0 & 0 & 0\\\\-2.0 & 1.0 & 5.0 & 0 & 0\\\\0 & -2.0 & 1.0 & 5.0 & 0\\\\0 & 0 & -2.0 & 1.0 & 5.0\\\\0 & 0 & 0 & -2.0 & 1.0\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "⎡1.0   5.0    0     0     0 ⎤\n",
       "⎢                           ⎥\n",
       "⎢-2.0  1.0   5.0    0     0 ⎥\n",
       "⎢                           ⎥\n",
       "⎢ 0    -2.0  1.0   5.0    0 ⎥\n",
       "⎢                           ⎥\n",
       "⎢ 0     0    -2.0  1.0   5.0⎥\n",
       "⎢                           ⎥\n",
       "⎣ 0     0     0    -2.0  1.0⎦"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The representation type of the diagonal matrix is:  <class 'scipy.sparse._dia.dia_matrix'>\n",
      "  (1, 0)\t-2.0\n",
      "  (2, 1)\t-2.0\n",
      "  (3, 2)\t-2.0\n",
      "  (4, 3)\t-2.0\n",
      "   (0, 0)\t1.0\n",
      "   (1, 1)\t1.0\n",
      "   (2, 2)\t1.0\n",
      "   (3, 3)\t1.0\n",
      "   (4, 4)\t1.0\n",
      "   (0, 1)\t5.0\n",
      "   (1, 2)\t5.0\n",
      "   (2, 3)\t5.0\n",
      "   (3, 4)\t5.0\n"
     ]
    }
   ],
   "source": [
    "diagonal = sparse.diags([-2, 1, 5], [-1, 0, 1], shape=(5, 5))\n",
    "display_matrix(diagonal.toarray())\n",
    "print(\"The representation type of the diagonal matrix is: \", type(diagonal))\n",
    "sympy.pprint(diagonal)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Example: Solving the linear system:       \n",
    "$$A x = b$$\n",
    "\n",
    "- **Using spsolve**\n",
    "\n",
    "- **Using splu** (based on LU decomposition [link](https://en.wikipedia.org/wiki/LU_decomposition) )\n",
    "\n",
    "A *SparseEfficiencyWarning* is raised if the sparse matrix is not converted in the most efficient format.\n",
    "So it is better to convert from \"dia\" to \"csr\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The unknown 'x' is exactly what we expected:\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}1.0\\\\2.0\\\\3.0\\\\4.0\\\\5.0\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "⎡1.0⎤\n",
       "⎢   ⎥\n",
       "⎢2.0⎥\n",
       "⎢   ⎥\n",
       "⎢3.0⎥\n",
       "⎢   ⎥\n",
       "⎢4.0⎥\n",
       "⎢   ⎥\n",
       "⎣5.0⎦"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}1.0\\\\2.0\\\\3.0\\\\4.0\\\\5.0\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "⎡1.0⎤\n",
       "⎢   ⎥\n",
       "⎢2.0⎥\n",
       "⎢   ⎥\n",
       "⎢3.0⎥\n",
       "⎢   ⎥\n",
       "⎢4.0⎥\n",
       "⎢   ⎥\n",
       "⎣5.0⎦"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "A = 3 * sparse.eye(5)\n",
    "x = np.array([[i] for i in range(1, 6)])\n",
    "b = A @ x\n",
    "\n",
    "print(\"The unknown 'x' is exactly what we expected:\")\n",
    "# Solution by spsolve\n",
    "display_matrix(spsolve(A.tocsr(), b))\n",
    "\n",
    "# Solution by LU decomposition\n",
    "A_LU = splu(A.tocsc())\n",
    "display_matrix(A_LU.solve(b))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='sec3'></a>\n",
    "## Numerical solution of the PDE\n",
    "\n",
    "Ok, let us solve the vector equation derived in the section [\"Implicit discretization\"](#sec1.2).\n",
    "\n",
    "In this case we consider a call option with strike $K$, maturity $T$. The stock price $S_0$ is not relevant for the algorithm. We will use it in the end to compute the value of the option for $S_0$.\n",
    "\n",
    "A common practice is to choose the computational region between $3K$ and $K/3$. Then we have $A_1 = \\log K/3$ and $A_2 = \\log 3K$.\n",
    "\n",
    "The values of the parameter are:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = 0.1\n",
    "sig = 0.2\n",
    "S0 = 100\n",
    "X0 = np.log(S0)\n",
    "K = 100\n",
    "Texpir = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "Nspace = 3000  # M space steps\n",
    "Ntime = 2000  # N time steps\n",
    "\n",
    "S_max = 3 * float(K)\n",
    "S_min = float(K) / 3\n",
    "\n",
    "x_max = np.log(S_max)  # A2\n",
    "x_min = np.log(S_min)  # A1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "x, dx = np.linspace(x_min, x_max, Nspace, retstep=True)  # space discretization\n",
    "T, dt = np.linspace(0, Texpir, Ntime, retstep=True)  # time discretization\n",
    "Payoff = np.maximum(np.exp(x) - K, 0)  # Call payoff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "V = np.zeros((Nspace, Ntime))  # grid initialization\n",
    "offset = np.zeros(Nspace - 2)  # vector to be used for the boundary terms\n",
    "\n",
    "V[:, -1] = Payoff  # terminal conditions\n",
    "V[-1, :] = np.exp(x_max) - K * np.exp(-r * T[::-1])  # boundary condition\n",
    "V[0, :] = 0  # boundary condition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# construction of the tri-diagonal matrix D\n",
    "sig2 = sig * sig\n",
    "dxx = dx * dx\n",
    "\n",
    "a = (dt / 2) * ((r - 0.5 * sig2) / dx - sig2 / dxx)\n",
    "b = 1 + dt * (sig2 / dxx + r)\n",
    "c = -(dt / 2) * ((r - 0.5 * sig2) / dx + sig2 / dxx)\n",
    "\n",
    "D = sparse.diags([a, b, c], [-1, 0, 1], shape=(Nspace - 2, Nspace - 2)).tocsc()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Backward iteration\n",
    "for i in range(Ntime - 2, -1, -1):\n",
    "    offset[0] = a * V[0, i]\n",
    "    offset[-1] = c * V[-1, i]\n",
    "    V[1:-1, i] = spsolve(D, (V[1:-1, i + 1] - offset))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comment:\n",
    "\n",
    "You may be tempted to think: \n",
    "\n",
    "*Why not calculating the inverse of the matrix **D**? Something like this:*\n",
    "\n",
    "```python\n",
    "from scipy.sparse.linalg import inv\n",
    "Dinv = inv(D)\n",
    "# and inside the block of the backward iteration substitute\n",
    "V[1:-1,i] = Dinv * (V[1:-1,i+1] - offset)\n",
    "```\n",
    "\n",
    "Well... you can try... It works, but it is very slow!  \n",
    "\n",
    "The reason is that **inv** computes the sparse inverse of A (using the same spsolve method). However the inverse of a tridiagonal matrix, in general, is not sparse!!  \n",
    "For this reason, all the efficiency gained by using scipy.sparse is lost.\n",
    "\n",
    "We will see very soon that the right approach is instead to use **splu**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Here we are!\n",
    "\n",
    "we can now find the value for $S_0 = 100$ and plot the curve at $t_0$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13.269144076064563\n"
     ]
    }
   ],
   "source": [
    "# finds the option at S0\n",
    "oPrice = np.interp(X0, x, V[:, 0])\n",
    "print(oPrice)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "S = np.exp(x)\n",
    "fig = plt.figure(figsize=(15, 6))\n",
    "ax1 = fig.add_subplot(121)\n",
    "ax2 = fig.add_subplot(122, projection=\"3d\")\n",
    "\n",
    "ax1.plot(S, Payoff, color=\"blue\", label=\"Payoff\")\n",
    "ax1.plot(S, V[:, 0], color=\"red\", label=\"BS curve\")\n",
    "ax1.set_xlim(60, 170)\n",
    "ax1.set_ylim(0, 50)\n",
    "ax1.set_xlabel(\"S\")\n",
    "ax1.set_ylabel(\"price\")\n",
    "ax1.legend(loc=\"upper left\")\n",
    "ax1.set_title(\"BS price at t=0\")\n",
    "\n",
    "X, Y = np.meshgrid(T, S)\n",
    "ax2.plot_surface(Y, X, V, cmap=cm.ocean)\n",
    "ax2.set_title(\"BS price surface\")\n",
    "ax2.set_xlabel(\"S\")\n",
    "ax2.set_ylabel(\"t\")\n",
    "ax2.set_zlabel(\"V\")\n",
    "ax2.view_init(30, -100)  # this function rotates the 3d plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the BS_pricer class there are more methods implemented. Let us have a look:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from FMNM.Parameters import Option_param\n",
    "from FMNM.Processes import Diffusion_process\n",
    "from FMNM.BS_pricer import BS_pricer\n",
    "\n",
    "# Creates the object with the parameters of the option\n",
    "opt_param = Option_param(S0=100, K=100, T=1, exercise=\"European\", payoff=\"call\")\n",
    "\n",
    "# Creates the object with the parameters of the process\n",
    "diff_param = Diffusion_process(r=0.1, sig=0.2)\n",
    "\n",
    "# Creates the object of the pricer\n",
    "BS = BS_pricer(opt_param, diff_param)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default the method PDE_price uses the LU solver. Let us compare the execution times of three implementations:\n",
    "- splu\n",
    "- Thomas: I wrote a wrapper of a LAPACK function.  [code](./src/FMNM/Solvers.py)\n",
    "- spsolve\n",
    "- SOR: I wrote the SOR solver in cython, such that it would be easily integrated with the python code. However it is still very slow. [Cython code](./src/FMNM/cython/solvers.pyx)\n",
    "  From this cell it is also not possible to modify the SOR parameters (if you want to modify it, you need to enter the *BS.PDE_price* method in [BS_pricer class](./src/FMNM/BS_pricer.py) ). \n",
    "  \n",
    "The SOR and Thomas algorithms are explained in the notebook **A1** and the code optimization for the SOR algorithm is the topic of the notebook **A2**. Further comparisons (with cython and C code) are in the notebook A2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Price: 13.269418 Time: 0.404955\n",
      "Price: 13.269418 Time: 0.651605\n",
      "Price: 13.269418 Time: 3.438750\n",
      "Price: 13.269418 Time: 24.499912\n"
     ]
    }
   ],
   "source": [
    "print(\"Price: {0:.6f} Time: {1:.6f}\".format(*BS.PDE_price((5000, 4000), Time=True, solver=\"splu\")))\n",
    "print(\"Price: {0:.6f} Time: {1:.6f}\".format(*BS.PDE_price((5000, 4000), Time=True, solver=\"Thomas\")))\n",
    "print(\"Price: {0:.6f} Time: {1:.6f}\".format(*BS.PDE_price((5000, 4000), Time=True, solver=\"spsolve\")))\n",
    "print(\"Price: {0:.6f} Time: {1:.6f}\".format(*BS.PDE_price((5000, 4000), Time=True, solver=\"SOR\")))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Thomas and SPLU are super fast!!!**\n",
    "\n",
    "\n",
    "A put option can be priced by changing the terminal and boudary conditions. You can read the implementation code inside \"BS_pricer\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "price:  3.7532654035665085\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "put_param = Option_param(S0=100, K=100, T=1, exercise=\"European\", payoff=\"put\")\n",
    "# Creates the object of the put pricer\n",
    "put = BS_pricer(put_param, diff_param)\n",
    "print(\"price: \", put.PDE_price((5000, 4000)))\n",
    "put.plot(axis=[50, 140, 0, 50])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### References\n",
    "\n",
    "[1] Björk Tomas (2009). Arbitrage theory in continuous time, Oxford Financial Press. \n",
    "\n",
    "[2] Wilmott Paul (1994). Option pricing: Mathematical models and computation. Oxford Financial Press."
   ]
  }
 ],
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   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
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 },
 "nbformat": 4,
 "nbformat_minor": 2
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